Development of flexible and adaptable fault detection and diagnosis algorithm for induction motors based on self-organization of feature extraction

Authors:
Hyeon Bae;Sungshin Kim;Jang-Mok Kim;Kwang-Baek Kim
Affiliations:
School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;Dept. of Computer Engineering, Silla University, Busan, Korea
Venue:
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

In this study, the datamining application was achieved for fault detection and diagnosis of induction motors based on wavelet transform and classification models with current signals. Energy values were calculated from transformed signals by wavelet and distribution of the energy values for each detail was used in comparing similarity. The appropriate details could be selected by the fuzzy similarity measure. Through the similarity measure, features of faults could be extracted for fault detection and diagnosis. For fault diagnosis, neural network models were applied, because in this study, it was considered which details are suitable for fault detection and diagnosis.